This study investigated personalized adaptive learning, teacher education, and self-efficacy to determine if personalized adaptive teacher education can increase self-efficacy. It is suggested that teachers with higher self-efficacy tend to stay in the teaching profession longer. Chapters 2 and 3 are literature reviews on personalizing adaptive learning to determine what common components are used in personalized adaptive learning systems to get a clear understanding of what previous literature suggests building this study on it. Chapter 4 investigates the data collected from 385 teachers to understand better what teachers report on factors that increase their self-efficacy. As a result, it was found that teachers' self-efficacy increases with more training, support, and resources. In chapter 5, a framework was developed based on previous findings, with components of personalized adaptive learning to provide support/help at the right time for teachers to increase their self-efficacy. An empirical study was conducted to validate this framework, where the framework was used as a guide to personalize and adapt summer teacher preservice training and survey teachers on their self-efficacy before and after the training to see its impact on teachers' self-efficacy. However, since summer preservice training was virtual, the framework could not be implemented fully, as we were not able to observe teachers' behaviors and monitor their learning to provide them help and support, as needed and being in the post-COVID-19 year as educators dealing with about two-year learning loss systemwide, seems decreased teachers' self-efficacy. The findings of this study can guide preservice teacher education institutions and decision-makers of teacher education to assess inservice teachers' needs and self-efficacy to help and support them with a more personalized adaptive education to improve their self-efficacy.

Contextual information retrieval (CIR) is a critical technique for today’s search engines in terms of facilitating queries and returning relevant information. Despite its importance, little progress has been made in its application, due to the difficulty of capturing and representing contextual information about users. This thesis details the development and evaluation of the contextual SERL search, designed to tackle some of the challenges associated with CIR from the World Wide Web. The contextual SERL search utilises a rich contextual model that exploits implicit and explicit data to modify queries to more accurately reflect the user’s interests as well as to continually build the user’s contextual profile and a shared contextual knowledge base. These profiles are used to filter results from a standard search engine to improve the relevance of the pages displayed to the user. The contextual SERL search has been tested in an observational study that has captured both qualitative and quantitative data about the ability of the framework to improve the user’s web search experience. A total of 30 subjects, with different levels of search experience, participated in the observational study experiment. The results demonstrate that when the contextual profile and the shared contextual knowledge base are used, the contextual SERL search improves search effectiveness, efficiency and subjective satisfaction. The effectiveness improves as subjects have actually entered fewer queries to reach the target information in comparison to the contemporary search engine. In the case of a particularly complex search task, the efficiency improves as subjects have browsed fewer hits, visited fewer URLs, made fewer clicks and have taken less time to reach the target information when compared to the contemporary search engine. Finally, subjects have expressed a higher degree of satisfaction on the quality of contextual support when using the shared contextual knowledge base in comparison to using their contextual profile. These results suggest that integration of a user’s contextual factors and information seeking behaviours are very important for successful development of the CIR framework. It is believed that this framework and other similar projects will help provide the basis for the next generation of contextual information retrieval from the Web.

Title from first page of PDF document. Includes bibliographical references (p. 32-37).

Industrial Control Systems (ICSs) are used to monitor and control critical infrastructure such as electricity and water. ICS were originally stand-alone systems, but are now widely being connected to corporate national IT networks, making remote monitoring and more timely control possible. While this connectivity has brought multiple benefits to ICS, such as cost reductions and an increase in redundancy and flexibility, ICS were not designed for open connectivity and therefore are more prone to security threats, creating a greater requirement for adequate security engineering approaches. The culture gap between developers and security experts is one of the main challenges of ICS security engineering. Control system developers play an important role in building secure systems; however, they lack security training and support throughout the development process. Security training, which is an essential activity in the defence-indepth strategy for ICS security, has been addressed, but has not been given sufficient attention in academia. Security support is a key means by which to tackle this challenge via assisting developers in ICS security by design. This thesis proposes a novel framework, the Industrial Control System Security Engineering Support (ICS-SES), which aims to help developers in designing secure control systems by enabling them to reuse secure design patterns and improve their security knowledge. ICS-SES adapts pattern-based approach to guide developers in security engineering, and an automated planning technique to provide adaptive on-the-job security training tailored to personal needs. The usability of ICS-SES has been evaluated using an empirical study in terms of its effectiveness in assisting the design of secure control systems and improving developers’ security knowledge. The results show that ICS-SES can efficiently help control system designers to mitigate security vulnerabilities and improve their security knowledge, reducing the difficulties associated with the security engineering process, and the results have been found to be statically significant. In summary, ICS-SES provides a unified method of supporting an ICS security by design approach. It fosters a development environment where engineers can improve their security knowledge while working in a control system production line.

Developing personalized cancer therapies based on cancer genomics methodologies forms the basis for future cancer therapeutics. A genomics platform was developed based on canine cancer to produce a proof-of-concept for personalized genomics led therapeutic choices but also developing personalized therapeutics for canine cancer patients themselves. The platform identified the genetic state of a canine cancer patient within two drugable pathways; p53 and HSP90/IRF1. The former gene was wild-type p53 thus directing the use of p53 activating molecules. The latter mutations in both HSP90 and IRF1 suggested an investigation into HSP90 and interferon signalling molecules as drug leads. Drugs that target both of these pathways were subsequently used to measure drug effects in cell line models but also to identify novel biomarkers of drug responses. My study focused on the effect of the HSP90-inhibitor Ganetespib had on its client proteins, particularly IRF-1. Briefly my results indicated the following:(i) Ganetespib downregulated IRF-1 protein levels in A375 cell lines and this attenuation was not mediated by either MDM2 or CHIP (E3 ligase). IFNγ- induced IRF-1 was also observed to be downregulated when Ganetespib was used in combination therapy.(ii) Insitu proximity ligation assay showed induced HSC70 upregulation upon HSP90 inhibition by Ganetespib and HSC70/MDM2 complexes were seen to be stabilized compared to the usage of MDM2/p53 inhibitor-nutlin. I hypothesize that MDM2/HSC70 complex might chaperon IRF-1 into lysosome for degradation via chaperon mediated autophagy pathway. (iii) My results also indicate that Ganetespib can downregulate IFN γ- induced PDL-1 expression in melanoma cell lines. Pre-sensitizing the cells with Ganetespib prior to the addition of IFNγ could attenuate PDL-1 to basal levels. (iv) My results also showed that the downregulation of PDL-1 by Ganetespib is an IRF-1 dependent mechanism. Therefore, my results suggest that HSP90 represents an important emerging target for cancer therapy because its inactivation results in the simultaneous blockade of multiple signalling pathways and can also sensitize tumor cells to other anticancer agents. Targeting HSP90 could also help to disrupt PD1/PDL- 1 interaction and activate immune system to recognise tumor cells. I conclude that HSP90 and IRF-1 play a critical role in types II interferon pathways and these findings establish a novel basis for the design of future Ganetespib-based combinatorial approaches to improve patient outcomes in this disease. These approaches finally demonstrate that cancer genomics can stratify choice of cancer drugs used on patients but also provide evidence that cancer patient samples can be used for the specific stratification of cancer drug choice based on cancer genomics data.

The main aim of this research was to delve into the minds of English as a foreign language students and try to find the right amount of guidelines provided by the teacher and materials while using constructivism represented by communicative approach and Dogme methodologies. As these classes were individual, the teacher’s role was to adjust the amount of constructivism and independence according to the student’s preference, which was expressed during interviews in which they reflected on their learning journey. The study also examined if and how the students’ initial level and previous learning experience influenced their willingness and ability to take charge of their learning and direct it based on their needs, moving away from prepared curriculum in form of a textbook or as decided by the teacher.   The findings showed virtually no difference between students on different levels of proficiency and all students were able to deal with a high level of constructivism and effectively direct their learning. Their learning curve showed a notable improvement especially at the beginning; all of this while achieving their goals and especially feeling more confident about their abilities. Another conclusion is that their preference was towards spoken practice of new language in a personalized context of relevant topics, giving them the opportunity to build fluency, practice previous topics and learn new vocabulary at the same time.

É apresentado como problema atual na área de Publicidade e Propaganda a exibição de conteúdo personalizado para nichos específicos. A propaganda acaba se concentrando nos produtos de maior sucesso, tendo como foco alguns públicos-alvo previamente determinados. Assim, o estudo pretende avaliar um novo canal para exibição de propaganda em nível individual, através das perspectivas da Cauda Longa e da Realidade Aumentada (R.A.). O ponto principal desse trabalho é, portanto, mostrar a quantidade de nichos existentes e sugerir uma personalização da propaganda a um nível praticamente individual, como uma ligação direta entre produto e consumidor, usando como suporte tecnológico a R.A. A partir da análise de trabalhos de Romero Tori, Paul Milgram, Ronald Azuma, Cláudio Kirner, Robson Siscouto, Lúcia Santaella, entre outros autores, foi feita uma avaliação dos principais pontos de interesse da tecnologia de Realidade Aumentada para profissionais da área do Design. Discorrendo não só sobre suas características como também identificando problemas e questionamentos atuais, tanto para esses profissionais como para o consumidor final. A tese se enquadra na linha de pesquisa de Design e Tecnologia ao passo que os profissionais desta área de conhecimento são responsáveis diretos por pensar as formas de interação de modo que sejam mais orgânicas e intuitivas para os usuários. Existe muito trabalho a ser realizado, no tocante à Ergonomia e Usabilidade, nas soluções que usam a Realidade Aumentada, principalmente na forma como se dá a sobreposição da informação digital ao ambiente real capturado pela câmera e sua manipulação pelo indivíduo. / Show custom content for specific market segments is a current problem in the Advertising industry. The advertisement ends up prioritizing the most successful products, with the focus on some predetermined audiences. With that in mind, this study evaluates a new channel for advertising on an individual level, through the perspectives of the Long Tail and Augmented Reality (AR). For that reason, the main point of this work is demonstrate the amount of existing niches and suggest customized ads to an individual level, as a direct link between product and consumer, using as technological support AR. From the analysis of the authors Romero Tori, Paul Milgram, Ronald Azuma, Claudio Kirner, Robson Siscouto, Lucia Santaella, among others, an assessment was made about the main points of interest in AR to the field of professional designers. Reflecting not only about their characteristics as well as identifying current problems and questions , both for these professionals and to the final consumer. The work fits the Design and Technology area because their professionals are directly responsible for thinking ways of interaction in order to be more intuitive for users. There is much more work to be done with regards to Ergonomics and Usability in the solutions that use Augmented Reality, especially in the way the digital information overlays the environment captured by the camera and its manipulation by the individual.

abstract: Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. The other problem is that their effectiveness is less when information for a graph is uncertain. For an uncertain graph, they require exponential computation costs to calculate ranking scores with considering all possible worlds. In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

In the past few years, the general work habits of people have changed dramatically, raising concerns about their well-being. Numerous health-related problems have been observed from their health records such as obesity, diabetes or heart diseases, and unhealthy eating is one of its factors. But these problems can be prevented if people start eating healthy food. The population, in general, is also realizing that healthy eating is important for their well-being. However, they usually resist because they assume that healthy food is not tasty and they do not want to comprise their taste preferences. Moreover, they have various other considerations that become barriers for them while selecting a healthy recipe. These are:(1) their complex, restrained needs (i.e., allergies and nutritional goals), (2) their strict lifestyle or dietary preferences (i.e., their desire to eat only vegan or vegetarian food), (3) lack of knowledge about how to choose healthy recipes while exploiting their taste preferences, (4) choosing recipes that maximize the use of available ingredients in their kitchen. Numerous researchers have been working in this field and developed various applications and systems to suggest healthy recipes. Apart from unhealthy eating, household food wastage has become a public problem, and some of the causes, which trigger it are users’ taste preferences (i.e., disliking of the food), and not cooking food before ingredients expiry dates. Thus, we propose a personalized recipes recommender system as a proof of concept called CAPRECIPES, which is based on context-awareness. It tackles the aforementioned barriers and improves the users’ experiences by providing the recommendations of personalized recipes with minimal efforts while exploiting their dynamically changing contexts. CAPRECIPES also helps in the reduction of food wastage as it first shows the recipes, which contain the ingredients that are expiring soon and matches with users’ taste preferences. It also considers that recipes do not violate users’ health restrictions and nutritional goals, and use the maximum number of available ingredients in users’ kitchen. The proposed system gathers users’ taste preferences by exploiting two third-party social media applications (i.e., Facebook and YouTube) and collaborative-based filtering algorithm. This thesis also explores various natural language processing techniques such as text analysis and parts of speech tagging to identify the recipes’ preferences and to find the most relevant match for each recipe or ingredient having different names. / Graduate

When the elections are getting closer, media starts to reports more frequently aboutpolitical parties, leaders and issues to enlighten the public. But the media changes just as time changes as well. Framing and entertainment/ advertising tends to be more common in political media in our time. Studies in media globalization and researches from the newest election in USA shows that the political media has become more personalized, which, according to some hasan effect on the public’s attitude or belief towards politics and leaders. In that very situation, I got interested in finding out if and how the political communication with personalization vs. party-orientated as leading question, has changed in Sweden and USA from the beginning of the millennium to 2016. As help, framing has been used to find entertainment, personalization and party-oriented terms in 200 news articles. The findings and conclusions are as following; the political medias have changed to a new agenda, with framing and entertainment setting political news in a new light and perspective.